Top Data & Analytics Jobs

Analyzes manufacturing, process, quality, and yield data to identify trends, anomalies, and improvement opportunities. Develops dashboards, visualizations, automated reports, statistical models, and machine learning solutions to investigate yield loss and process variation. Collaborates with engineering and manufacturing teams on process optimization and completes an end-to-end analytics project delivering measurable improvements in yield, cycle time, reporting, or decision support.

Leads development, implementation, documentation, and governance of statistical, predictive, and machine learning models for auto finance. Develops solutions for risk assessment, loss forecasting, pricing optimization, and portfolio performance. Advises senior leaders, partners with risk and governance teams, supports validation and regulatory compliance, and communicates analytical recommendations to stakeholders.

Lead design and implementation of scalable data solutions and pipelines using Python, SQL, and PySpark. Partner with stakeholders to translate requirements, perform deep-dive analysis on healthcare and claims data, design data models and transformations, build dashboards, and deploy AI/LLM-assisted workflows. Ensure data quality, compliance, governance, and mentor junior analysts.

Provides analytics-driven strategic advice for mergers, acquisitions, and other transactions. Responsibilities include analyzing contract data, developing algorithms and predictive models, integrating data sources, building pipelines, creating dashboards, validating data integrity and security, and identifying risks and compliance indicators. The role also involves client collaboration, mentoring junior staff, and delivering data-driven insights using Python and machine learning.

Paid, full-time 11-week summer internship for a rising senior pursuing a bachelor’s degree. The intern will analyze customer reliability issues, research escalations, manage resolution through compliance checks and internal coordination, and identify trends using data and AI/ML research methods. The program also includes mentorship, networking, professional development, community service, and cross-functional projects.

Paid, full-time summer intern supporting enterprise database environments across on-premises and cloud platforms. Responsibilities include assisting with Oracle, SQL Server, and NoSQL administration; database provisioning, patching, migrations, tuning, troubleshooting, security compliance, and audit support. The intern will also gain exposure to AWS Redshift, data warehouses, cloud operations, scripting, monitoring, automation, APIs, dashboards, and machine learning while collaborating with engineering and analytics teams.

Leads data analytics projects that generate insights for strategic decision-making. Develops visualization solutions with Power BI and Tableau, builds data pipelines, applies statistical modeling and predictive analytics, and manages data integrity and security. Collaborates with clients on tailored data solutions while coaching analytical teams, managing accounts, overseeing project success, and identifying business opportunities.

Lead advanced risk, fraud, and payment optimization analyses for major fintech merchants. Analyze large-scale transactional, ML output, and labeled risk data; design experiments and statistical investigations; develop scalable analytical tools, ETL workflows, dashboards, and reusable frameworks; collaborate with risk product teams; translate findings into merchant strategies and product improvements; enable commercial stakeholders through playbooks and self-service tools; and mentor analysts.

Analyze business data, uncover operational improvement opportunities, and develop automation and self-service solutions. The role partners with stakeholders and subject matter experts, conducts ad hoc analysis, delivers actionable insights, and communicates data-driven recommendations to management. It requires exposure to LLMs, machine learning, AWS, Snowflake, APIs, and multiple analytics tools. This is an on-site role requiring five days per week in the office.

Conduct healthcare economics research and quantitative analysis using clinical, financial, claims, and operational data. Develop SQL and Python queries, statistical models, performance metrics, dashboards, visualizations, and financial reports. Apply data governance and quality practices, identify medical cost and utilization trends, and provide actionable recommendations to business and leadership stakeholders. Lead analytical projects, collaborate with partners, perform ad hoc analyses, and present findings in leadership-ready formats.

Leads the Managed Services analytics roadmap, KPI governance, Power BI dashboard strategy, reporting automation, and AI-augmented metrics services on InterSystems IRIS. Provides technical leadership in SQL, ObjectScript, APIs, data models, interoperability, query optimization, and application performance. Establishes data-quality and reporting standards, develops predictive analytics, mentors analysts and engineers, investigates complex issues, and advises senior leadership on operational performance, risks, and investment priorities.

Develops regulatory compliance analytics and reporting solutions using SQL, dashboards, governed metrics, semantic layers, and AI-enabled business intelligence. Builds and maintains Tableau or similar dashboards, automates reporting with Tableau and Alteryx, evaluates Machine Learning and GenAI opportunities, supports prompt development, and translates stakeholder needs into analytical products. Collaborates with Compliance, Risk, Technology, Finance, and senior governance leaders to improve regulatory engagement insights, adoption, and firm-wide decision-making.

Monitor fraud trends in retail deposit acquisition, analyze large datasets, identify defensive gaps, and develop strategies balancing fraud losses, operational costs, and customer impact. Partner with cross-functional and technical product teams to deliver fraud prevention capabilities, present actionable insights to senior leadership, and support business growth while protecting customers.

Leads the design, development, and maintenance of enterprise data systems supporting manufacturing data, analytics, reporting, and AI. Oversees scalable cloud-based solutions, data pipelines, infrastructure, modeling, formats, and frameworks. Partners with stakeholders to align data capabilities with business needs and manages a high-performing data engineering team through coaching, performance management, and organizational development.

Analyze large datasets to identify digital fraud trends, develop loss-reduction strategies and business rules, automate fraud alert processes, monitor emerging risks, and present actionable insights to senior leadership. Collaborate with technical and business teams on fraud-fighting products, apply experimentation and machine learning, manage financial impacts, and improve customer experience across digital payment channels.

Leads data-driven strategic initiatives, performs root-cause analysis and data mapping, and identifies automation opportunities. Develops ETL workflows, REST APIs, dashboards, KPI reporting, and actionable insights from structured and unstructured data. Collaborates cross-functionally with operations, technology, finance, compliance, and controls teams. Manages Agile data analytics projects, improves processes, and presents business cases, metrics, and technical findings to senior management.

Manage point-of-sale fraud strategies and conduct complex risk analyses to reduce losses while balancing customer impact. Responsibilities include developing fraud rules and policies, identifying risk indicators, enhancing reporting, testing mitigation processes, leading strategy initiatives end-to-end, analyzing large datasets, and presenting recommendations to management. The role collaborates with Operations, IT, Risk Management, and other cross-functional partners to improve fraud detection and customer experience.

Semiconductor yield analysis internship focused on reviewing experimental data, identifying device performance shifts and process anomalies, improving measurement accuracy, and using inline data and machine learning to predict cell performance. The intern will collaborate with process integration, process engineering, and data science teams while developing root-cause analysis, process improvement, and technical communication skills.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Leads healthcare analytics service delivery across Medicare, Medicaid, and Commercial programs. Designs analytic models, dashboards, reports, and predictive solutions; translates complex data into client-ready insights; guides data governance and management practices; and promotes AI, machine learning, generative AI, and automation to improve reporting efficiency. Partners with clients, analysts, developers, subject matter experts, and AI teams to analyze claims, enrollment, provider, and member data and support value-based payment initiatives.

Lead a global analytics team to transform financial and operational data into actionable insights. Partner with stakeholders to align analytics with strategy, build scalable Power BI dashboards integrated with Snowflake, apply predictive modeling and ML, enforce data governance, drive automation with Python and Power Automate, and improve data architecture and reporting to inform decision-making and business performance.

Develop and deploy analytical and AI-enabled tools integrated with the Aladdin platform to support portfolio construction, investment research, relative value analysis, risk budgeting, due diligence, and portfolio monitoring. Partner with portfolio managers and engineering teams to identify opportunities, prototype solutions, manage delivery, and productionize scalable applications. Communicate insights clearly while applying knowledge of financial markets, asset classes, Python, SQL, and modern technology stacks.

Leads data governance and ownership initiatives, defining data requirements, quality standards, metadata, lineage, controls, and curated datasets. Performs SQL-based testing, validation, reconciliation, and root-cause analysis. Partners with technology and business teams to mitigate data risk, support analytics and machine learning use cases, and improve data products. Applies AI and LLM tools to automate governance activities and contributes to roadmap, agile delivery, and stakeholder management.

Lead quantitative analytics initiatives by integrating and preparing diverse customer, financial, digital, account, and operational datasets. Analyze customer journeys, identify friction points, optimize collections features, develop KPIs, and apply statistical, machine learning, and predictive modeling techniques. Translate strategic objectives into analytics solutions, collaborate with product and technology teams, and present actionable insights to business and executive stakeholders.

Partner with Total Rewards stakeholders to design and execute analytics that evaluate employee wellbeing and benefits program ROI. Combine and validate data from claims, utilization, and other sources, build forecasting and advanced-analytics models, and deliver dashboards and executive summaries. Lead project workstreams, mentor junior analysts, and ensure data quality, compliance, and actionable insights to inform benefits strategy.